Module 5 Visualization Lab - Stefano Pinzon

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products%>%
  filter(str_detect(product_category, "ORGANIC")) %>%
  inner_join(transactions, by = "product_id") %>%
  inner_join(demographics, by = "household_id") %>%
  group_by(income) %>%
  summarize(total = sum(sales_value)) %>%
  ggplot(aes(x = income, y= total)) +
  geom_bar(stat="identity", width = .4, fill = "#FF6666") + 
  scale_y_continuous(name = "Total Amount of Money Spent", labels = scales::dollar) +
  labs(
    title = "Income Ranges Spendature on Organic Products",
    subtitle= "Note this used transactions_sample data for ease of plotting",
    x = "Income Range"
  ) 

transactions%>%
  inner_join(products, by = "product_id") %>%
  filter(str_detect(product_category, "ORGANIC")) %>%
  group_by(week) %>%
  summarize(total = sum(sales_value)) %>%
  ggplot(aes(x=week, y = total, color = total)) +
  geom_point(show.legend = FALSE, alpha = 1, size = 4) +
  geom_smooth() +
  scale_y_continuous(name = "Total Sales", labels = scales::dollar) +
  labs( x = "Week", title = "Sales of Organic Products Per Week", subtitle = "Data used is transaction_sample for computing reasons")

products %>%
  filter(str_detect(product_category, "ORGANIC")) %>%
  distinct(product_id) %>%
  inner_join(transactions, by = "product_id") %>%
  group_by(month = months(transaction_timestamp)) %>%
  summarize(total = sum(sales_value)) %>%
  ggplot(aes(x = month, y = total, group= 1)) +
  geom_point (color = "red") +
  geom_line(color = "blue") + 
  scale_x_discrete(limits = month.name) +
  labs(
    title = "Amount of Organic Products Sold Per Month",
    subtitle = "Not full data for rendering purposes",
    x = "Month",
    y = "Amount of Organic Producs Sold" 
  )